Github code corpus. As a general corpus of Python code, we employ the ETH Py150k dataset [1]} containing over 150k program files from popular Python projects. The dataset is split into 100k files for training and 50k files for testing. During our evaluation, we employ the same split and, hence, train only on Python functions obtained from train split. The test split is used for evaluating the performance on mutants. We extract all top level functions and deduplicate the datasets such that the Python functions used for training only occur once in the training dataset and do not occur in the test split. In total, our training corpus contains more than 360k Python function implementations (after filtering).
